ABSTRACT
The COVID-19 pandemic has created an unprecedented health and financial crisis across the world. This crisis has not shown any sign of abatement despite world-wide efforts to contain the spread of the virus with draconian measures such as extensive social distancing, travel restrictions, and lockdowns. An inherent difficulty in controlling this crisis has disrupted the entire global supply chain and consequently threatened our livelihood and freedom. Faced with this daunting crisis, many businesses regardless of their sizes desperately need a viable survival strategy. To fill such a need, this paper reinvents crisis management tools and develops a strategy map for mitigating the adverse impact of COVID-19 on global supply chain operations, while enhancing supply chain resilience in times of turbulence and uncertainty. Copyright © 2023 Inderscience Enterprises Ltd.
ABSTRACT
Numerous studies have been conducted to identify the effects of natural crises on supply chain performance. Conventional analysis methods are based on either manual filter methods or data-driven methods. The manual filter methods suffer from validation problems due to sampling limitations, and data-driven methods suffer from the nature of crisis data which are vague and complex. This study aims to present an intelligent analysis model to automatically identify the effects of natural crises such as the COVID-19 pandemic on the supply chain through metadata generated on social media. This paper presents a thematic analysis framework to extract knowledge under user steering. This framework uses a text-mining approach, including co-occurrence term analysis and knowledge map construction. As a case study to approve our proposed model, we retrieved, cleaned, and analyzed 1024 online textual reports on supply chain crises published during the COVID-19 pandemic in 2019-2021. We conducted a thematic analysis of the collected data and achieved a knowledge map on the impact of the COVID-19 crisis on the supply chain. The resultant knowledge map consists of five main areas (and related sub-areas), including (1) food retail, (2) food services, (3) manufacturing, (4) consumers, and (5) logistics. We checked and validated the analytical results with some field experts. This experiment achieved 53 crisis knowledge propositions classified from 25,272 sentences with 631,799 terms and 31,864 unique terms using just three user-system interaction steps, which shows the model's high performance. The results lead us to conclude that the proposed model could be used effectively and efficiently as a decision support system, especially for crises in the supply chain analysis.